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Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify “Breast Cancer Disease” tumor using neural network with Feed-forward Backpropagation Algorithm to classify...
In this paper, appropriate and efficient networks for breast cancer knowledge discovery from clinically collected data sets are investigated. Invoking various data mining techniques, it is desired to find out the percentage of disease development, using the developed network. The results, help in choosing a reasonable treatment of the patient. Several neural network structures are evaluated for this...
Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses...
Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Early detection of primary tumor is an essential and effective method to reduce mortality. Computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing abnormalities earlier and faster than traditional screening methods. This paper presents a new approach for detecting...
A major class of problems in medical science involves the diagnosis of a disease based upon various tests performed upon the patient. Cancer is a complex and clinical heterogeneous disease. The research into the diagnosis and treatment of cancer has become an important issue for the scientific community. The objective of cancer classification is to design a classifier to categorize the tissue samples...
This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as ldquorecurrencerdquo or ldquorelapserdquo or ldquodisease freerdquo + ldquoneural networkrdquo + ldquocancerrdquo. Increasing of the predictive...
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